Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Sushil, Madhumita"'
Autor:
Dorfner, Felix J., Dada, Amin, Busch, Felix, Makowski, Marcus R., Han, Tianyu, Truhn, Daniel, Kleesiek, Jens, Sushil, Madhumita, Lammert, Jacqueline, Adams, Lisa C., Bressem, Keno K.
Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this approach remains unclear. This study evaluates the performance of biomedica
Externí odkaz:
http://arxiv.org/abs/2408.13833
Autor:
Miao, Brenda Y., Chen, Irene Y., Williams, Christopher YK, Davidson, Jaysón, Garcia-Agundez, Augusto, Sun, Shenghuan, Zack, Travis, Saria, Suchi, Arnaout, Rima, Quer, Giorgio, Sadaei, Hossein J., Torkamani, Ali, Beaulieu-Jones, Brett, Yu, Bin, Gianfrancesco, Milena, Butte, Atul J., Norgeot, Beau, Sushil, Madhumita
Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift
Externí odkaz:
http://arxiv.org/abs/2403.02558
Autor:
Sushil, Madhumita, Zack, Travis, Mandair, Divneet, Zheng, Zhiwei, Wali, Ahmed, Yu, Yan-Ning, Quan, Yuwei, Butte, Atul J.
Although supervised machine learning is popular for information extraction from clinical notes, creating large annotated datasets requires extensive domain expertise and is time-consuming. Meanwhile, large language models (LLMs) have demonstrated pro
Externí odkaz:
http://arxiv.org/abs/2401.13887
Autor:
Mehandru, Nikita, Miao, Brenda Y., Almaraz, Eduardo Rodriguez, Sushil, Madhumita, Butte, Atul J., Alaa, Ahmed
Recent developments in large language models (LLMs) have unlocked new opportunities for healthcare, from information synthesis to clinical decision support. These new LLMs are not just capable of modeling language, but can also act as intelligent "ag
Externí odkaz:
http://arxiv.org/abs/2309.10895
Autor:
Sushil, Madhumita, Kennedy, Vanessa E., Mandair, Divneet, Miao, Brenda Y., Zack, Travis, Butte, Atul J.
Both medical care and observational studies in oncology require a thorough understanding of a patient's disease progression and treatment history, often elaborately documented in clinical notes. Despite their vital role, no current oncology informati
Externí odkaz:
http://arxiv.org/abs/2308.03853
We aimed to investigate the impact of social circumstances on cancer therapy selection using natural language processing to derive insights from social worker documentation. We developed and employed a Bidirectional Encoder Representations from Trans
Externí odkaz:
http://arxiv.org/abs/2306.09877
Autor:
Sushil, Madhumita, Butte, Atul J., Schuit, Ewoud, van Smeden, Maarten, Leeuwenberg, Artuur M.
Objective: Text mining of clinical notes embedded in electronic medical records is increasingly used to extract patient characteristics otherwise not or only partly available, to assess their association with relevant health outcomes. As manual data
Externí odkaz:
http://arxiv.org/abs/2301.06570
Most research studying social determinants of health (SDoH) has focused on physician notes or structured elements of the electronic medical record (EMR). We hypothesize that clinical notes from social workers, whose role is to ameliorate social and e
Externí odkaz:
http://arxiv.org/abs/2212.01462
Developing a general-purpose clinical language inference model from a large corpus of clinical notes
Several biomedical language models have already been developed for clinical language inference. However, these models typically utilize general vocabularies and are trained on relatively small clinical corpora. We sought to evaluate the impact of usi
Externí odkaz:
http://arxiv.org/abs/2210.06566
Autor:
Sushil, Madhumita, Butte, Atul J., Schuit, Ewoud, van Smeden, Maarten, Leeuwenberg, Artuur M.
Publikováno v:
In Journal of Clinical Epidemiology March 2024 167
